A novel way of determining gestational age upon the birth of a child
Bibliographic record
Abstract
reterm birth is the leading cause of infant morbidity and mortality globally [1].Determining whether an infant was born preterm can be challenging, especially in low-resource settings due to a paucity of prenatal care and dating ultrasounds, the unreliability of recall of last menstrual period, and recognised limitations of Ballard score and other newborn clinical assessment and anthropometric measurements [2].Accurate estimation of gestational age (GA) is important in informing the medical care of the newborn and accurately assessing neurocognitive development.GA dating is also necessary for population-level determination of preterm birth rates as well as appropriate resource and intervention allocations.There is a need for better data that provide robust estimates of the burden of preterm birth in low-resource settings. OPPORTUNITIES FROM METABOLIC GA TESTINGRecently, novel methods for establishing an infant's GA have emerged that may be able to overcome some of the existing limitations.One such approach involves using an established public health procedure, a heel prick blood spot typically used for newborn screening [3].Newborn screening is a routine practice in many high-income countries, wherein a blood spot is collected via heel prick from newborns to screen them for a variety of rare, treatable conditions.The blood spot is analysed to determine levels of several analytes including acylcarnitines, amino acids, and endocrine markers.Abnormalities in specific analytes or ratios of analytes may suggest an underlying disorder.Infants who are identified as "screen positive" for one or more of the screened conditions undergo confirmatory testing and, ideally, receive prompt, definitive treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".